融合新闻情绪与深度学习，预测印度股市30年历史数据
News-Driven Stock Price Forecasting in Indian Markets: A Comparative Study of Advanced Deep Learning Models
- 用多变量LSTM、Prophet+LightGBM、SARIMA三种模型预测股价
- 结合推特和财经媒体情绪分析，提升预测准确性
- 适合关注金融科技与量化投资的从业者参考
由于影响股价变动的因素众多，股票市场预测仍是交易员、分析师和工程师面临的复杂挑战。人工智能（AI）与自然语言处理（NLP）的最新进展显著提升了股价预测能力。本文基于印度国家证券交易所提供的30年历史数据，利用先进的深度学习模型——包括多变量多步长短期记忆网络（LSTM）、经Optuna优化的Facebook Prophet与LightGBM组合，以及季节性自回归积分滑动平均模型（SARIMA），进行股价预测。同时，我们整合了来自推特及《商业标准》《路透社》等可靠金融来源的情绪分析结果，以捕捉新闻对股价波动的关键影响。
原文摘要 · Abstract (English)
Forecasting stock market prices remains a complex challenge for traders, analysts, and engineers due to the multitude of factors that influence price movements. Recent advancements in artificial intelligence (AI) and natural language processing (NLP) have significantly enhanced stock price prediction capabilities. AI's ability to process vast and intricate data sets has led to more sophisticated forecasts. However, achieving consistently high accuracy in stock price forecasting remains elusive. In this paper, we leverage 30 years of historical data from national banks in India, sourced from the National Stock Exchange, to forecast stock prices. Our approach utilizes state-of-the-art deep learning models, including multivariate multi-step Long Short-Term Memory (LSTM), Facebook Prophet with LightGBM optimized through Optuna, and Seasonal Auto-Regressive Integrated Moving Average (SARIMA). We further integrate sentiment analysis from tweets and reliable financial sources such as Business Standard and Reuters, acknowledging their crucial influence on stock price fluctuations.
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